Meta-analyses are an incredibly useful tool for synthesising evidence. However, such analyses typically use aggregate data, meaning the average scores or outcomes for treatment groups, which can cause problems if we’re trying to dig a little deeper into the question of ‘what works’ to answer ‘what works, and for whom?’

The ‘for whom?’ question is especially important in terms of implications for practice. Doctors and patients faced with a choice of treatment might be concerned that reviews only provide summary findings, telling us about the average treatment effect for a whole population. Patients might want to know if the treatment is more or less likely to work for them, taking into account their personal characteristics (such as age and gender) and their specific illness profile (for example, how severe their depression is or how long they’ve been suffering from it.) In fact, we can use trials to answer these questions, by looking at whether there is a moderator effect on the outcome.

Moderator analyses can provide us with more detail about the effectiveness of a treatment for a specific patient

A moderator is a variable that impacts on the strength of an effect – in this case, whether a treatment is effective or not. The easiest way to think of a moderator (although a little dull!) is as a light dimmer switch. The switch itself is a particular treatment or intervention, and the light is the effect of that treatment – in this case, an improvement in symptoms of depression. In terms of our ‘what works, for whom?’ question, we can think of most trials as evaluating simply whether the switch works. Does flicking the switch (giving someone the treatment) actually turn on the light (reduce symptoms of depression)? Moderator analyses go a step further to ask whether any factors act as a dimmer, making the light brighter or weaker, i.e. making the treatment more or less effective.

However, the problem with checking for these kinds of effects in typical meta-analyses is that using only the summary statistics from the individual trials can be misleading. Using the individual patient data from each trial is a way around this, and this is exactly what Nelson and colleagues have done in order to look at moderators of antidepressant treatment in older people.

Methods

In an IPD (individual patient data) analysis, the researchers conducting the meta-analysis ask the authors of each trial in the review to provide their original data sets for the analysis. This means that rather than having to take average scores from the trials, the authors are able to combine the individual data from each patient in each trial into one overall data set. This gives them much greater statistical power to detect effects, and also avoids a problem called ‘the ecological fallacy’ (whereby aggregate data can obscure moderator effects).

The authors had conducted a previous review of trials comparing antidepressants to placebo in patients aged 60 or above, who had major depressive disorder and were living in the community. They identified 10 trials, and were able to access the data on all 10. Seven of those trials contained the information they were looking for on moderators (specifically, they looked at age, age of onset, sex, single episode vs recurrent depression, initial depression severity and cognitive impairment). The 7 trials included a total of 2,488 patients of whom 1,494 received antidepressants and 994 received a placebo.

Results

Duration of illness and severity of illness were both found to be significant moderators

Unsurprisingly, overall they found that antidepressants were more effective than placebo

Turning to the question of moderator effects:

Course of illness (single episode or recurrent depression), sex, and age were not significantly related to the treatment group-response interaction

Duration of illness was found to be significant (having a significant interaction with treatment group and depression outcome)

Severity was also a significant moderator, but only for those patients with an illness duration of more than 10 years

In both cases, the effect of antidepressants (compared to placebo) was greater in those groups.

Conclusions

The authors concluded:

Our findings suggest that the duration of depressive illness moderates antidepressant response in patients over age 60. Patients with an illness duration >10 years showed greater drug-placebo differences, while those with a short illness duration showed no drug effect.

The reviewers say that “clinicians should reconsider the prevailing practice of simply prescribing an antidepressant in these patients”

This suggests that not only is the brightness turned down when there has been short illness duration, but you may as well have turned the light off. The authors report no effect of antidepressants for those patients who had depression for less than 2 years. Adding in severity to those with a longer illness duration further narrows down the subgroup of patients who showed benefit, with the authors noting that this subgroup would only comprise 385 patients. Although we can’t be certain that the equivalent numbers would be found in a typical primary care population, this would suggest that in fact only a minority of older people with depression show benefit from antidepressants. As the authors state, “Clinicians should reconsider the prevailing practice of simply prescribing an antidepressant in these patients”.

For those with a long illness duration and at least moderate severity however, antidepressants have a beneficial effect, demonstrating how analyses such as these can help us better tailor treatment recommendations to specific patients who will show most benefit.

Limitations

It’s worth noting the significant variation in effect sizes in the studies included in this meta-analysis

The authors were not able to look at cognitive status as a moderator due to limitations in how the data was reported, with some studies only reporting the threshold rather than the actual scores. This demonstrates that even with IPD analyses where reviewers are accessing the ‘raw’ data, inconsistencies in the way that measures are used or findings reported can still disrupt our ability to synthesise data

Similarly, the authors comment that assessing illness duration was complicated as trials didn’t provide detail on how this was assessed. They suggest the significance of illness duration in this analysis indicates that duration should be more reliably and routinely collected in future trials

Finally, it should be noted that there was large effect of the study covariate on response (meaning effect size varied a lot between studies), suggesting unexplained heterogeneity in the included studies. Although this was controlled for in the analyses, it might indicate that important factors underlying variation were missed out.

Sarah is a Research Fellow with the NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) Greater Manchester at the University of Manchester. She is a health researcher with a particular focus on evaluating mental health treatments and services. She works on a variety of randomised controlled trials, systematic reviews and qualitative studies. Her main research interests are implementation research, e-health and mental health technologies, co-morbidity of mental and physical health problems, moderators of treatment effects and patient and public involvement in research.